Genetic Algorithms in Matrix Representation and Its Application in Synthetic Data

Yingrui Chen, Mark Elliot, Joseph Sakshaug

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

105 Downloads (Pure)


This paper is the implementation of an earlier position paper (Chen, Elliot & Sakshaug, 2016) and explains how to use a new form of genetic algorithms (matrix GAs) to generate synthetic data and provides a proof of concept using a small individual-level microdata set. The new method is able to iteratively optimise synthetic data based on a set of utility parameters until its difference from the original data achieves a desired level. The paper describes the advantages of this method and its potential in synthetic data production. It covers theoretical and computerised model design and specifies further development of this study.
Original languageEnglish
Title of host publicationUNECE Worksession on Statistical Confidentiality 2017
Publication statusAccepted/In press - 31 Jul 2017

Research Beacons, Institutes and Platforms

  • Cathie Marsh Institute
  • Manchester Institute for Collaborative Research on Ageing


Dive into the research topics of 'Genetic Algorithms in Matrix Representation and Its Application in Synthetic Data'. Together they form a unique fingerprint.

Cite this